Database virtualization aims to consolidate databases on storage devices. Database virtualization decentralizes the database and masks the physical location and configuration of a database from applications. A virtualized database can be stored on a number of computers, in multiple locations and on multiple types of database software.
While virtualization technology has simplified database management and improved the performance, availability, flexibility, efficiency and processing speed of databases, there is currently no means to sample, model, and forecast for database virtualization. Existing physical-to-virtual (P2V) and capacity planner toolsets generate Virtual Machine templates based on generic workload and standardized resource computation resulting in tentative, costly, and error-prone adoption of database virtualization.
A need therefore exists for a database virtualization modeler/wizard that models, forecasts, and generates virtual machine configuration files against monitored databases. Yet another need exists for a database virtualization modeler/wizard that enables interactive and proactive analysis of database virtualization configuration and layout using a combination of gathered performance metrics, embedded and encapsulated best practices, and criteria selection.